eBurnout_PYTHON_NotRealData.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# eBurnout Trial Dataset Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Created by Jesús García García - Master´s Degree in Big Data Analytics UEM"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USUARIO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>ESTADO_CIVIL</th>\n",
       "      <th>NUMERO_HIJOS</th>\n",
       "      <th>ALTURA</th>\n",
       "      <th>PESO</th>\n",
       "      <th>HORAS_DE_SUEÑO</th>\n",
       "      <th>EJERCICIO FISICO</th>\n",
       "      <th>LECTURA</th>\n",
       "      <th>TIEMPO_VIDA_LABORAL</th>\n",
       "      <th>ESPECIALIDAD</th>\n",
       "      <th>FUMADOR</th>\n",
       "      <th>VIVE_EN_PAREJA</th>\n",
       "      <th>BURNOUT</th>\n",
       "      <th>HOSPITAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>181.80</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Angiología y cirugía vascular</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.80</td>\n",
       "      <td>98.0</td>\n",
       "      <td>8.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>Medicina del trabajo</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>INFANTA SOFÍA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>169.30</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>Medicina interna</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.55</td>\n",
       "      <td>98.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>Oftalmología</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168.05</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Medicina intensiva</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>INFANTA SOFÍA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     USUARIO  SEXO  EDAD  ESTADO_CIVIL  NUMERO_HIJOS  ALTURA   PESO  \\\n",
       "0          1     1  28.0             1             0  181.80  120.0   \n",
       "19         2     1  29.0             0             0  166.80   98.0   \n",
       "45         3     1  30.0             1             0  169.30  120.0   \n",
       "104        4     1  31.0             1             0  165.55   98.0   \n",
       "130        5     1  32.0             1             0  168.05  120.0   \n",
       "\n",
       "     HORAS_DE_SUEÑO  EJERCICIO FISICO  LECTURA  TIEMPO_VIDA_LABORAL  \\\n",
       "0               9.0              40.0        1                  9.0   \n",
       "19              8.5              59.0        0                  7.0   \n",
       "45              9.0              85.0        1                  5.0   \n",
       "104             7.3             144.0        1                  3.0   \n",
       "130             7.4             170.0        1                  1.0   \n",
       "\n",
       "                      ESPECIALIDAD  FUMADOR  VIVE_EN_PAREJA   BURNOUT  \\\n",
       "0    Angiología y cirugía vascular      1.0                0        0   \n",
       "19            Medicina del trabajo      1.0                1        1   \n",
       "45                Medicina interna      0.0                1        1   \n",
       "104                   Oftalmología      1.0                1        0   \n",
       "130             Medicina intensiva      1.0                1        0   \n",
       "\n",
       "          HOSPITAL  \n",
       "0      SAN LLÁTZER  \n",
       "19   INFANTA SOFÍA  \n",
       "45     SAN LLÁTZER  \n",
       "104    SAN LLÁTZER  \n",
       "130  INFANTA SOFÍA  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "import sys\n",
    "sys._enablelegacywindowsfsencoding()\n",
    "%matplotlib inline\n",
    "dataset_path = r\"C:\\Users\\Jesús García\\Desktop\\TFM\\eBurnout Dataset Prueba\\eburnout_NotRealData.csv\"\n",
    "df = pd.read_csv(dataset_path,  error_bad_lines=False)\n",
    "\n",
    "#Sort df by user id\n",
    "df.sort_values(\"USUARIO\", inplace=True)\n",
    "#Show dataset head\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Change Hospital and Especialidad names to id for processing data\n",
    "df['HOSPITAL'] = df['HOSPITAL'].replace(['SAN LLÁTZER', 'INFANTA SOFÍA'],[1, 2])\n",
    "df['ESPECIALIDAD']= df['ESPECIALIDAD'].replace(['Alergología','Anestesiología y reanimación','Angiología y cirugía vascular','Cardiología','Dermatología médico-quirúrgica y venereología','Endocrinología','Gastroenterología','Geriatría','Ginecología y obstetricia o tocología','Hematología y hemoterapia','Infectología','Medicina aeroespacial','Medicina de urgencias','Medicina del deporte','Medicina del trabajo','Medicina familiar y comunitaria','Medicina física y rehabilitación','Medicina intensiva','Medicina interna','Medicina legal y forense','Medicina preventiva y salud pública','Medicina veterinaria','Nefrología','Neumología','Neurología','Nutriología','Odontología','Oftalmología','Oncología médica','Oncología radioterápica','Otorrinolaringología','Pediatría','Psiquiatría','Rehabilitación','Reumatología','Siquiatria','Toxicología','Traumatología','Urología'],[1 ,2 ,3 ,4 ,5 ,6 ,7 ,8 ,9 ,10 ,11 ,12 ,13 ,14 ,15 ,16 ,17 ,18 ,19 ,20 ,21 ,22 ,23 ,24 ,25 ,26 ,27 ,28 ,29 ,30 ,31 ,32 ,33 ,34 ,35 ,36 ,37 ,38,39])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USUARIO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>ESTADO_CIVIL</th>\n",
       "      <th>NUMERO_HIJOS</th>\n",
       "      <th>ALTURA</th>\n",
       "      <th>PESO</th>\n",
       "      <th>HORAS_DE_SUEÑO</th>\n",
       "      <th>EJERCICIO FISICO</th>\n",
       "      <th>LECTURA</th>\n",
       "      <th>TIEMPO_VIDA_LABORAL</th>\n",
       "      <th>ESPECIALIDAD</th>\n",
       "      <th>FUMADOR</th>\n",
       "      <th>VIVE_EN_PAREJA</th>\n",
       "      <th>BURNOUT</th>\n",
       "      <th>HOSPITAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
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       "      <td>98.0</td>\n",
       "      <td>8.5</td>\n",
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       "      <td>0</td>\n",
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       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>169.30</td>\n",
       "      <td>120.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.55</td>\n",
       "      <td>98.0</td>\n",
       "      <td>7.3</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168.05</td>\n",
       "      <td>120.0</td>\n",
       "      <td>7.4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
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      ],
      "text/plain": [
       "     USUARIO  SEXO  EDAD  ESTADO_CIVIL  NUMERO_HIJOS  ALTURA   PESO  \\\n",
       "0          1     1  28.0             1             0  181.80  120.0   \n",
       "19         2     1  29.0             0             0  166.80   98.0   \n",
       "45         3     1  30.0             1             0  169.30  120.0   \n",
       "104        4     1  31.0             1             0  165.55   98.0   \n",
       "130        5     1  32.0             1             0  168.05  120.0   \n",
       "\n",
       "     HORAS_DE_SUEÑO  EJERCICIO FISICO  LECTURA  TIEMPO_VIDA_LABORAL  \\\n",
       "0               9.0              40.0        1                  9.0   \n",
       "19              8.5              59.0        0                  7.0   \n",
       "45              9.0              85.0        1                  5.0   \n",
       "104             7.3             144.0        1                  3.0   \n",
       "130             7.4             170.0        1                  1.0   \n",
       "\n",
       "     ESPECIALIDAD  FUMADOR  VIVE_EN_PAREJA   BURNOUT  HOSPITAL  \n",
       "0               3      1.0                0        0         1  \n",
       "19             15      1.0                1        1         2  \n",
       "45             19      0.0                1        1         1  \n",
       "104            28      1.0                1        0         1  \n",
       "130            18      1.0                1        0         2  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 1349 entries, 0 to 667\n",
      "Data columns (total 16 columns):\n",
      "USUARIO                1349 non-null int64\n",
      "SEXO                   1349 non-null int64\n",
      "EDAD                   1349 non-null float64\n",
      "ESTADO_CIVIL           1349 non-null int64\n",
      "NUMERO_HIJOS           1349 non-null int64\n",
      "ALTURA                 1349 non-null float64\n",
      "PESO                   1349 non-null float64\n",
      "HORAS_DE_SUEÑO         1349 non-null float64\n",
      "EJERCICIO FISICO       1349 non-null float64\n",
      "LECTURA                1349 non-null int64\n",
      "TIEMPO_VIDA_LABORAL    1349 non-null float64\n",
      "ESPECIALIDAD           1349 non-null int64\n",
      "FUMADOR                1349 non-null float64\n",
      "VIVE_EN_PAREJA         1349 non-null int64\n",
      "BURNOUT                1349 non-null int64\n",
      "HOSPITAL               1349 non-null int64\n",
      "dtypes: float64(7), int64(9)\n",
      "memory usage: 179.2 KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x20e7f027a58>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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E/0Euijq6q99v/9S9Eb6vfbH1EeTwx6OAjdThysexZ1kMS7gxxootZv5Ia5DTqC4pTcaN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\nlfj/wCA1CtzYbYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02d4f4e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check missing data\n",
    "sns.heatmap(df.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02ea6588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Check total burnout\n",
    "sns.countplot(y=\"BURNOUT\", data=df)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Show...Peso, Ejercicio Físico\n",
      "Show...Altura, Ejercicio Físico\n",
      "Show...Ejercicio Físico, Tiempo_vida_laboral\n",
      "Show...Fumador, Ejercicio Físico\n",
      "Show...Edad, Ejercicio Físico\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x20e03538e10>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l7eHXcKHRXe9UVV2DBcuLYDKZ3OamrK07KMWnHsLUIG8Q6gsjpAblNAYlybN+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\npbXNneoxm0xwhjDtI9d1O1jMnDa5g8pPzUJhYbGlEDFVpJr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OFtufdo7GmfO1OPP5JdVj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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02eb24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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/9KyEJ2dnSUeuxNaSsELKk/r1AQDu2ozcCLzXJ2cAcPUwhNBwpRKa8+eWb9wm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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e03063cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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nmM1mTc0FJG0JDgrU9TGqi4X6fW5E7ia7ZfXEE0/AbDZj586dWLBgAZo2bapU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\nSMCjjz4Kg8GAhx9+GKdPn4b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ete8BoHfv3vjyyy9x9uxZALcaQnJyMo4dO1btfs899xxW\nr16Ny5cv2+43d+5cREdHO/w8f/rpJyQlJaGsrAwAbNP9rVPQAcjW7+fnhyeffBJLliyBxWIBcKvB\npqWl2S77QaRn3LIixdy+GxAA3n777WrDQqsOL719N+CTTz5ZbXI4cOtSF1OmTEFiYiK2bNmCN998\nE7Nnz8b69etRXl6Odu3aYc6cOdV+x2g0Ijk5Ga+99hokSUJxcTF69+6NmJiYavcLDQ1FQkICEhIS\nUFlZiYqKCoSHh8vuuqzLE088gRMnTiAqKgqNGzeGJEmYMmVKjV2UcvVPnjwZy5cvt71Wfn5+mDNn\nTo1dmkR6xEG2REQkPO4GJCIi4bFZERGR8NisiIhIeGxWREQkPDYrIiISHpsVEREJj82KiIiE938L\nPFS+RTdeXwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e032e6748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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DI6wAAIZHWAEADI+wAgAYHmEFADA8wgoAYHiEFQDA8AgrAIDhEVYAAMMjrAAA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\nGTNG3bp1U5cuXVxSa2PzhL8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e033f5cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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fZFhdvHjR9PWWLVusDqvg4GBs27YNABAWFoatW7e2e01SUhKSkpKs2i8RkRGn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\nCY9hRUREwmNYERGR8P4/TBVRQBWTHQ4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e03518c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot some health comparison\n",
    "sns.set_palette(\"GnBu_d\")\n",
    "sns.set_style('whitegrid')\n",
    "# Peso, Ejercicio Físico\n",
    "print(\"Show...Peso, Ejercicio Físico\")\n",
    "sns.jointplot(x='PESO',y='EJERCICIO FISICO',data=df)\n",
    "print(\"Show...Altura, Ejercicio Físico\")\n",
    "sns.jointplot(x='ALTURA',y='EJERCICIO FISICO',data=df)\n",
    "print(\"Show...Ejercicio Físico, Tiempo_vida_laboral\")\n",
    "sns.jointplot(x='EJERCICIO FISICO',y='TIEMPO_VIDA_LABORAL',data=df)\n",
    "print(\"Show...Fumador, Ejercicio Físico\")\n",
    "sns.jointplot(x='FUMADOR',y='EJERCICIO FISICO',data=df)\n",
    "print(\"Show...Edad, Ejercicio Físico\")\n",
    "sns.jointplot(x='EDAD',y='EJERCICIO FISICO',data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear Regression Physical exercise in working life"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Train Test Split\n",
    "X = df[['EJERCICIO FISICO']]\n",
    "y = df['TIEMPO_VIDA_LABORAL']\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=101)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training the Model\n",
    "from sklearn.linear_model import LinearRegression\n",
    "# Create an instance of a LinearRegression() model named lm.\n",
    "lm = LinearRegression()\n",
    "# Train/fit lm on the training data.\n",
    "lm.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 0.07518466]\n"
     ]
    }
   ],
   "source": [
    "# Print out the coefficients of the model\n",
    "print('Coefficients: \\n', lm.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Predicted Y')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Hp556CkeOHEHLHY1IWlpaEBUVpfh3IraL8wStnqJan81qtXL77Nnp87H2zW2w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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e04aa7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predicting Test Data\n",
    "# Use lm.predict() to predict off the X_test set of the data.\n",
    "predictions = lm.predict( X_test)\n",
    "# Create a scatterplot of the real test values versus the predicted values. \n",
    "plt.scatter(y_test,predictions)\n",
    "plt.xlabel('Y Test')\n",
    "plt.ylabel('Predicted Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fnnJvVlGtPTX6UVo2a8L8//tabhtYAaQdqqGfTpzktwsX6du9i5wPL6o9Oztb\npj//JBezL7N83bdqx7E4UvLVzE2dnj2HkmjR1A2fNg+pHUeIcuHb9mEe7RvIv7d8z9UbMvZ8eZKS\nr0YURWHHf4drHdy7hww8JizKi+NDqK114uCxFBmSuBxJyVcj6Rm/kvHbOXp37iSHaYTFcXHW8sqT\noVy+doOfTpxUO47FkJKvJvIM+ew6+CONG9TDv7232nGEqBADe3XHrX499v6YhE5/S+04FkFKvppI\nSEpGdyuXgT27y9k0wmJZWVnRzactBYWFxB74Ue04FkGGNaiCil+2fe1mDoePn8DX2wu3hg9+0dO9\nXHpe1S6XrwnU2OZV8edcW+tEgF9H4g//xJmz59WOU+3JLmEVpygKh46fwF6jIbCLn9pxhKgUXTq2\np26d2vywP5E8g0HtONWa2ZI3Go3MmDGDMWPGEBYWRmZmZpHp0dHRjBo1ipCQEOLi7uwVXL16laef\nfprQ0FCmTp1Kbm4uAHPmzGHUqFGEhYURFhZGTo6cKmXOidP/4dKVa/Tp4odDrVpqxxGiUtja2DAg\noBvXb+YQ9e13asep1syWfGxsLAaDgbVr1/KPf/yDyMhI07Ts7GyioqJYs2YNy5cv56OPPsJgMPD5\n558zbNgwvvnmG9q2bcvatWsBSE1NZdmyZURFRREVFYWzs5whcjd5BgNxiYepV8dFzokXNU7LZk1o\n27oVX2/8jrNZl9SOU22ZLfmkpCR69eoFgK+vLykpKaZpx44do1OnTmg0GpydnXF3dyc9Pb3Ia3r3\n7s2BAwcwGo1kZmYyY8YMxo4dy/r16ytolSzH/qSf0d3KpVuHtvJlq6iRgrt1xtbWhgXLo+Tc+ftk\n9otXnU6HVqs1PbaxsaGgoABbW1t0Ol2RvXEnJyd0Ol2R552cnMjJyeHWrVuMHz+ep556isLCQiZM\nmED79u1p06bNX94zLS2tPNatiNu3b1fIcitCVtZFrt3M4cjxE3i1aI6L1pGsrJLHk6/q8vPz7yt7\n8Z+VuWWU9LN90G1mLntZM1am+93uxVXGOhZ/j+LZRwb15JttsUSt/5bO7dv8JUNV+lxXxZ4xW/Ja\nrRb9n8Z6NhqN2NraljhNr9fj7Oxser5WrVro9Xpq166Ng4MDEyZMwMHBAYBu3bqRnp5eYsl7e5f/\neeBpaWkVstyKkH42i92Hf8Jeo2FwnwCuX7uOm1tjtWPdl6ysi/eVvfjPqrSbppQ2/728xhxz2cua\nsTLd73YvrjLWsfh77DmcXCT7sODeHEn7hXU79zJ62CDcimWoSp9rtXomKan0G6ObPQbg5+dHfHw8\nAMnJyXh5eZmm+fj4kJSURF7GCOsoAAAVLUlEQVReHjk5OWRkZODl5YWfnx979+4FID4+Hn9/f379\n9VdCQ0MpLCwkPz+fo0eP0q5duwddN4t04vQZzmZdok9Xf/myVdR4tjY2vPHcRC5fu86yaBnArKzM\n7sn379+fhIQExo4di6IoREREsGLFCtzd3enbty9hYWGEhoaiKArh4eHY29szZcoUpk2bRnR0NK6u\nrixYsABHR0eGDx9OSEgIdnZ2jBgxgoceki8Ti9Ppb7E78QhuDevTUb5sFQKA9l6ejOgXSPR3PzBx\n1DAa1qurdqRqw2zJW1tbM3v27CLPeXp6mv4/JCSEkJCQItPr16/P8uXL/7Ks5557jueee+5+s9YI\ny6I3or+Vy2OD+soAZEL8yZTQx9lzKIkd+xIZP2KwfD7ukZyyUYWczjzLuu2xdGr7sNzOT4hiXJy1\nvDQ+hPOXfuf4ydNqx6k2ZFiDKkJRFD5c9jVaJ0d6d5YrW8uqKl6eL8rfkD49WblhC3GHjtC6RXMc\nHeQ7K3NkT76K+H7fAZLTTvHCE4/jUMte7ThCVEnW1tYM7NWdPIOBXQdlALN7ISVfBeTo9Xz69Vra\ntvZgeHBvteMIUaU1qOtKd18fUn85Q8Zv59SOU+VJyVcBn69ax/WbN3nj+YlyZasQ96C7nw/1Xevw\nffwB9Ldy1Y5TpUmjqCw57RQbd8YxduhA2ni0VDuOENWCrY0NQwIDyNHf4rNVa9WOU6VJyavIkJ9P\n5JIVNG5Qn+fGjFI7jhDVSpNGDejcoS0bf4jjaGq62nGqLCl5FUV9+x2/nrvAG89NkC9bhbgPvTp3\nommjBkQs/pLbeXlqx6mSpORV8uv5C6yM2UL/gK708OuodhwhqiWNnR3/nPw05y5e4v/WblQ7TpUk\nJa8Co9FI5BcrqGWvYepTT6gdR4hq7ZEObRnRL5B/b/2e5LRTasepcqTkVbBldzzJaad4ecJY6tVx\nUTuOENXe3yeOw61BfWYtWoJOf0vtOFWKlHwlu5h9hUVfr6FT24flnHghyomTgwOz/j6Z7CtX+XB5\nlNpxqhQZ1qASGY1GZn+2FMWo8PaLz8oAS9VcTRhKoTqtY4eHW/PUY4+yLPpbuvq2Z3DvALUjVQmy\nJ1+J1nz3A0dT0wl/6gmaNmqodhwhLM6Tox+lU9uHmbdkJWfOytWwICVfaX759TcWr15HYBc/hgX3\nUjuOEBbJ1saG98JfwNHBgX9+8Bn6XLkaVkq+Euj0t/jnh59Sp7aW6ZOeksM0QlSg+q51eG/qFM5e\nvEjE58sxGo1qR1KVlHwFUxSFOZ8vJ+v3y8x59UVcXWqrHUkIi+ff3psXnghh18HDLF9Xs28ZKF+8\nVrBvNm9nz6EjvDxhLB3beJl/gRCiXDzx6GB+PXeB5es20dytMYN691A7kiqk5CtQ/I9H+WxVNEHd\nHiF0+CC14whRo1hZWTHt+Sc5f+l35n6+nAZ1XfFv7612rEonh2sqSHrGf5jxyWK8PVsy8+Xn5Ti8\nECqws7Ml8vW/09ytEa9HfkzKqQy1I1U6KfkK8FvWRf7x/kLqODvzwbSp1LKXwceEUIuLs5ZFM97A\n1cWZVyMW8Muvv6kdqVJJyZez85d+56VZ8yg0Gln41j+o51pH7UhC1Hj1Xevw6cxp1LK358VZkaSc\nqjk3ApeSL0cXLmXz4sxIbufl8emMN2jVvKnakaqtb3fGFflPiAfVpGEDvnjvTWprnXh59nx+PJaq\ndqRKISVfTtJO/4dn33qPW7dvs2jGGzzU0l3tSEKIYpo0bMCS996iaaOGhM9dwIYdu9WOVOGk5MvB\nviM/MWVmBPYaO5bMeVtu4ydEFVbPtQ6LZ/+Trh3bM///vuL9L77EkJ+vdqwKIyX/AAoKCvg0ai2v\nR35My6ZNWDb3HVo1a6J2LCGEGc5OTnwwbSpPjh7Opti9PDVtFqf+k6l2rAohJX+fzpw9x6R3Ili9\naRujBgbzxXtvyZesQlQjNjbWTB73GB+9+So3cnQ8Nf1d/m/tBou7jaBcDFVG+txcvly/mTVbd+Dk\nUIuIf7xEcPfOascSQtynHn4dWf3RXBYsX8XydZvYsnsfLzzxOP0DumFjU/33g6Xk75FOf4t138ey\nZusObuToGN63Ny8+EUKd2s5qRxNCPCAXZy2zp05m1IAgFq5czaxFS/hy/SbGjxjCoN490NjZqR3x\nvknJ34WiKJw4fYYtu+P5YX8it3JvE+DXkWdCRtK2tYfa8YQQ5cy37cOsiJxFXOIRvtq4lYjFX/JZ\n1FoG9OzG4MAAvD1bYW1dvfbuzZa80Whk1qxZnDx5Eo1Gw5w5c2jRooVpenR0NGvWrMHW1pYpU6YQ\nFBTE1atXee2117h9+zYNGzbk/fffx8HBocR5q5obOTpSTp3mYPJx9h9J5mL2Zew1Gvr26ELI4H60\n8WyldkQhRAWytramb48uBHfvzJHjJ9i8ay+bd8Wz/vtd1HetQw+/jvi396ZjGy8aN6indlyzzJZ8\nbGwsBoOBtWvXkpycTGRkJIsXLwYgOzubqKgoYmJiyMvLIzQ0lICAAD7//HOGDRvGqFGjWLp0KWvX\nrmXo0KElzqvRaCp8JYvLMxi4kaPjwqVsfsu6yG8XLnI26xK/nrtA5oUsAOw1Gjr7tOWp0cPp270L\nWifHSs8phFCPlZUVnX3a0dmnHTd1ehKSktl/5Cd2HfyRzbv2AuBa25kWTd1o2awJLZq4YWUswM5R\ni2ttZ1xqO2NrY6PyWtxDySclJdGr1507Gfn6+pKSkmKaduzYMTp16oRGo0Gj0eDu7k56ejpJSUlM\nmjQJgN69e/PRRx/RvHnzEuf18fEplxXZGrePlFOnyS8oID+/AEN+wX//P5/8ggKuXL1GXkEBN3J0\n3M4zFN0ItjY0a9yIFk3dGBwYgE+bh2jb2oNa9pX/D5AQouqprXVicGAAgwMDKCgsJCPzHD+nn+J0\n5lkyz2ex51AS12/m3Jk5al2R17nWro2jQy1q2WuoZW9PLXsN9hoNNjbW2FjbYGNtjY2NNR29H6Z/\nQNdyz2625HU6HVqt1vTYxsaGgoICbG1t0el0ODv/74tHJycndDpdkeednJzIyckpdd6SJCUllXlF\n3Go74vZI+fyDodzWk5pyvFyWdT+a161918fViWRXR3XKXvzz3qWd112nF1+3++mL8uDZqC6ejeoC\nHcttmRWxLmZLXqvVotfrTY+NRiO2trYlTtPr9Tg7O5uer1WrFnq9ntq1a5c6b3H+/v4PtEJCCCH+\nx+zXxH5+fsTHxwOQnJyMl9f//pX18fEhKSmJvLw8cnJyyMjIwMvLCz8/P/buvXPMKj4+Hn9//1Ln\nFUIIUXGsFEVR7jbDH2fXnDp1CkVRiIiIID4+Hnd3d/r27Ut0dDRr165FURQmTZrEwIEDuXz5MtOm\nTUOv1+Pq6sqCBQtwdHQscV4hhBAVx2zJV2eFhYW8//77pKSkYDAYePnllwkKCiI5OZm5c+diY2ND\nz549eemll9SOWqqMjAxCQkI4cOAA9vb21SJ7Tk4Or7/+Ojqdjvz8fKZPn06nTp2qRXYwf9pwVZOf\nn8+bb77J+fPnMRgMTJkyhdatWzN9+nSsrKx46KGHmDlzZpU9v/vKlSuMGjWKL7/8Eltb22qTe8mS\nJezevZv8/HzGjRtHly5dqmZ2xYLFxMQoM2fOVBRFUS5evKisWLFCURRFefTRR5XMzEzFaDQqzz77\nrJKSkqJeyLvIyclRnnvuOaVbt27K7du3FUWpHtk/+eQT07bOyMhQRo4cqShK9ciuKIqyY8cOZdq0\naYqiKMpPP/2kTJ48WeVEd7d+/Xplzpw5iqIoytWrV5XAwEBl0qRJSmJioqIoivLOO+8oP/zwg5oR\nS2UwGJQXXnhBGTBggHL69OlqkzsxMVGZNGmSUlhYqOh0OmXRokVVNnsV+Gem4uzfv5/GjRvz/PPP\n8/bbbxMcHIxOp8NgMODu7o6VlRU9e/bk4MGDakf9C0VReOedd3j11VdxcHAAqDbZn3zyScaOHQvc\n+WvK3t6+2mSHu582XBUNGjSIV155xfTYxsaG1NRUunTpAtw5jfnAgQNqxburefPmMXbsWBo2bAhQ\nbXLv378fLy8vXnzxRSZPnkyfPn2qbHaLGdZg3bp1fPXVV0Wec3V1xd7eniVLlnD48GH++c9/smDB\ngiKnhDo5OXH27NnKjltESdmbNGnCkCFDaNOmjem54qezVtXsERER+Pj4kJ2dzeuvv86bb75ZJbOX\n5m6nDVdFTk5OwJ3cf//735k6dSrz5s0z3Tz+j9OYq5oNGzZQt25devXqxdKlS4E7OzdVPTfAtWvX\nuHDhAl988QXnzp1jypQpVTZ71fytvQ+PP/44jz/+eJHnwsPD6dOnD1ZWVnTp0oVff/21xFM5a9dW\n95zikrL379+fmJgYYmJiyM7O5umnn2bJkiXVIjvAyZMnefXVV3njjTfo0qULOp2uymUvzd1OG66q\nsrKyePHFFwkNDWX48OF88MEHpmlVdVvHxMRgZWXFwYMHSUtLY9q0aVy9etU0varmBqhTpw4eHh5o\nNBo8PDywt7fn4sWLpulVKbtFH67x9/c3ncqZnp6Om5sbWq0WOzs7fvvtNxRFYf/+/TzyyCMqJ/2r\nnTt3EhUVRVRUFA0aNODLL7+sNtlPnz7NK6+8woIFCwgMDASoNtnh7qcNV0WXL1/m6aef5vXXX+ex\nxx4DoG3bthw6dAi4cxpzVdzWq1evZtWqVURFReHt7c28efPo3bt3lc8Nd7pl3759KIrCpUuXyM3N\npXv37lUyu0WfXWMwGJg5cyYZGRkoisKsWbNo164dycnJREREUFhYSM+ePQkPD1c76l0FBwezfft2\n09k1VT37lClTOHnyJE2b3rmRuVarZfHixdUiO5R82rCnp6fasUo1Z84ctm/fjofH/0ZGfeutt5gz\nZw75+fl4eHgwZ84cbKrAOCqlCQsLY9asWVhbW/POO+9Ui9zz58/n0KFDKIpCeHg4zZo1q5LZLbrk\nhRCiprPowzVCCFHTSckLIYQFk5IXQggLJiUvhBAWTEpeCCEsmJS8EEJYsKp9GZ+o8iIjI0lNTSU7\nO5vbt2/TvHlzXF1dSUpKIiEhgU8//ZStW7eaxiYB6NGjB1OmTCE4OBgPDw+WLVtmmrZixQoiIyM5\nefLkX16bn59PeHg4Xbt2JT8/nyVLlnDgwAFsbGywtbVl6tSpdOxY8l16dDodAwYMYOfOnaZhAABG\njBjBJ598wjvvvMOsWbO4fPkyU6dOpXXr1iiKQkFBARMmTGDIkCGm12zbto0333yTHTt20KhRo7tu\nnz9f41DczJkz+fnnn/n2229Nz4WFhZGbm4uDgwO5ubkEBASYrie4evUq8+bN48KFCxQWFuLm5sb0\n6dNp0KABGzZsYNGiRTRv3hyAmzdv4ufnx8yZM03LXrp0KV9//TW7du0y5Zk+fTpDhgyhd+/ed10P\nUX1JyYsHMn36dODOOCRnzpzhtddeAyAgIMA0z5NPPsm4ceNKfP2lS5e4evUqdevWBWDv3r24uLiU\n+NqMjAxee+01Nm7cyKJFiygsLGTVqlVYW1tz/vx5Jk2axOLFi01F92darZagoCB27NjBqFGjAEhJ\nScHFxYWWLVsWmbdbt24sXLgQuHN5elhYGK1atcLb2xu4M17P+PHjiY6O5uWXXy7zNgPIzc3l6NGj\neHl5cejQIbp2/d+9PefNm4enpyeKohAaGsrx48dp3749L730Ek8//TT9+vUD4MCBA0yaNIl16+7c\nU3TYsGGm7W80Gk2v7dChAwBbtmxhyJAhfPfdd6ZtICyfHK4Rqho4cCDff/89cKfE3d3dsbOzK3He\n69ev4+joCMDmzZt59dVXTeN1N23alNDQUDZu3Fjqe4WEhBTZa46JiWHMmDF3zefk5MSYMWNMGc+e\nPcuNGzeYNGkSmzZtIj8//95X9k+2b99O9+7d+dvf/sbq1atLnMdgMFBQUEDDhg1JSUnB2dnZVPBw\n5y8id3d3Dh8+/JfX6vV6cnJyTLfYPHToEO7u7owdO7bU9xOWSUpeVLiVK1cSFhZm+i8hIcE0bdiw\nYWzfvh24U9zDhw8v8bUTJ05k5cqVvPfee1y5cgUXF5e/DBrWvHlzLly4UGqOjh07cuPGDbKysjAY\nDBw4cID+/fubzV+vXj2uXbsGwPr16xk9ejTOzs74+vqyc+fOe94Of7Zu3Toef/xxevTowYkTJ7h0\n6ZJp2rRp0wgLC2PgwIHUq1cPV1dXzp49W+JfKH9e561btzJ+/HgGDhzIxIkTmTx5sumvlD/e749B\ntX7++ef7yi2qHzlcIyrc3Q7XuLm5AXdGUTx69ChTp041+1qDwcCNGzf+MvxvZmamaXmleeyxx9i8\neTPNmjUjODgYjUZjNv+FCxdo3LgxhYWFbNmyhaZNm7J7925u3LjBqlWrihyvvxcZGRn88ssvREZG\nAmBlZcW///1v07r/cbjGaDTy5ptvsmzZMrp27cr58+f/sqzMzEx69OhBVlaW6XDN2bNnefbZZ00F\nf+PGDeLj47l69SpRUVHodDpWrVpV6vcXwrLInrxQ3ZAhQ4iMjKRTp06m8bjvRqPRMHjwYBYuXIjR\naATuHEb55ptvzB5rfvTRR4mNjWXLli2EhISYfS+dTse6desYNGgQe/fupX379kRFRbF8+XLWr1/P\nlStXSE9Pv7cV/a9169YRHh7O8uXLWb58OV999RUxMTEYDIYi81lbW9OoUSPy8/Px8/Pj8uXL7N69\n2zQ9Pj6ezMxM040q/tC8eXNmzpzJK6+8Qm5uLps3b2b06NF8+eWXLF++nOjoaBISEooM6yssl+zJ\niwq3cuVKtm3bZnrcqlUrZs+ebXo8aNAg5s6dW+R4uTmvvfYan376KSEhIdjZ2ZnuxVrSIY0/c3Fx\noVWrVly+fJlWrVqVOE9iYiJhYWFYW1tTWFjIyy+/jIeHB/Pnz//L2PmPPfYYq1ev5r333iv1Pf/8\nl8jgwYP57rvv2LRpk+m5Jk2a0KZNG3bs2AHcOVzzx93AatWqxQcffICVlRVffPEFERERLFmyBIDG\njRuzdOnSEkc67NGjBz169GDRokUkJCQwf/580zQHBwcGDBhAdHQ0AHPnzuXjjz8G7vxsFixYUPoG\nFNWOjEIphBAWTPbkhUUxGAw888wzf3m++F8P5WnXrl2sXLnyL89PmDDhnr7YFaIiyZ68EEJYMPni\nVQghLJiUvBBCWDApeSGEsGBS8kIIYcGk5IUQwoL9P0wl7J3yYqYmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02f01588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Residuals\n",
    "# Plot a histogram of the residuals and make sure it looks normally distributed. \n",
    "sns.distplot((y_test-predictions),bins=50);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 18.8947036379\n",
      "MSE: 500.675407112\n",
      "RMSE: 22.3757772404\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the Model\n",
    "from sklearn import metrics\n",
    "\n",
    "# Calculate the Mean Absolute Error, Mean Squared Error, and the Root Mean Squared Error. \n",
    "print('MAE:', metrics.mean_absolute_error(y_test, predictions))\n",
    "print('MSE:', metrics.mean_squared_error(y_test, predictions))\n",
    "print('RMSE:', np.sqrt(metrics.mean_squared_error(y_test, predictions)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Coeffecient</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>EJERCICIO FISICO</th>\n",
       "      <td>0.075185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Coeffecient\n",
       "EJERCICIO FISICO     0.075185"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Recreate the dataframe below. \n",
    "coeffecients = pd.DataFrame(lm.coef_,X.columns)\n",
    "coeffecients.columns = ['Coeffecient']\n",
    "coeffecients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Interpreting the coefficients:\n",
    "# Holding all other features fixed, a 1 unit increase in EJERCICIO FISICO is associated with an increase of 0.075185 TIEMPO_VIDA_LABORAL"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Logistic Regression (Predict whether or not a person can have burnout based off features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Logistic Regression\n",
    "#Split data into training set and testing set using train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "X = df[['PESO', 'ALTURA', 'HORAS_DE_SUEÑO','FUMADOR']]\n",
    "y = df['BURNOUT']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(903, 4)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=0, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Train and fit a logistic regression model on the training set\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "logmodel = LogisticRegression(random_state=0)\n",
    "logmodel.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[293   0]\n",
      " [153   0]]\n"
     ]
    }
   ],
   "source": [
    "#Print onfusion Matrix\n",
    "y_pred = logmodel.predict(X_test)\n",
    "from sklearn.metrics import confusion_matrix\n",
    "confusion_matrix = confusion_matrix(y_test, y_pred)\n",
    "print(confusion_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy of logistic regression classifier on test set: 0.66\n"
     ]
    }
   ],
   "source": [
    "#Get accuracy\n",
    "print('Accuracy of logistic regression classifier on test set: {:.2f}'.format(logmodel.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.66      1.00      0.79       293\n",
      "          1       0.00      0.00      0.00       153\n",
      "\n",
      "avg / total       0.43      0.66      0.52       446\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Anaconda3\\lib\\site-packages\\sklearn\\metrics\\classification.py:1135: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples.\n",
      "  'precision', 'predicted', average, warn_for)\n"
     ]
    }
   ],
   "source": [
    "#Predictions and Evaluations\n",
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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IEeI0+ff87rvvioxh5syZgkQiEU6fPq0wPSoqSpBIJMLAgQOLXEeHDh0EiUQijBs3TsjJ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\nJ4NDQKJy9erVguOmpiaqqqrQaDS9Ti+fTp5++mlGjhzJRx9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RlZsPZXJVqBJVaCcGk5LN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e03504860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Classifier Visualization playground\n",
    "from sklearn.decomposition import PCA\n",
    "X = df[['PESO', 'ALTURA', 'HORAS_DE_SUEÑO','FUMADOR']]\n",
    "y = df['BURNOUT']\n",
    "pca = PCA(n_components=2).fit_transform(X)\n",
    "X_train, X_test, y_train, y_test = train_test_split(pca, y, random_state=0)\n",
    "\n",
    "plt.figure(dpi=120)\n",
    "plt.scatter(pca[y.values==0,0], pca[y.values==0,1], alpha=0.5, label='YES', s=2, color='navy')\n",
    "plt.scatter(pca[y.values==1,0], pca[y.values==1,1], alpha=0.5, label='NO', s=2, color='darkorange')\n",
    "plt.legend()\n",
    "plt.title('Predict whether or not a person can have burnout based off features')\n",
    "plt.xlabel('Feature decomposition')\n",
    "plt.ylabel('Burnout')\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Principle Component Analysis (PCA) to visualize different features of people in hospitals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#features\n",
    "features = ['ALTURA', 'PESO', 'TIEMPO_VIDA_LABORAL']\n",
    "x = df.loc[:, features].values\n",
    "df['HOSPITAL'] = df['HOSPITAL'].replace([1,2 ],['SAN LLÁTZER', 'INFANTA SOFÍA'])\n",
    "#target\n",
    "y = df.loc[:,['HOSPITAL']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Standarize data\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "x = StandardScaler().fit_transform(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ALTURA</th>\n",
       "      <th>PESO</th>\n",
       "      <th>TIEMPO_VIDA_LABORAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.360966</td>\n",
       "      <td>2.763782</td>\n",
       "      <td>1.743744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.715787</td>\n",
       "      <td>1.374323</td>\n",
       "      <td>1.654459</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.156650</td>\n",
       "      <td>2.763782</td>\n",
       "      <td>1.565174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.495355</td>\n",
       "      <td>1.374323</td>\n",
       "      <td>1.475889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.936218</td>\n",
       "      <td>2.763782</td>\n",
       "      <td>1.386604</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ALTURA      PESO  TIEMPO_VIDA_LABORAL\n",
       "0  3.360966  2.763782             1.743744\n",
       "1  0.715787  1.374323             1.654459\n",
       "2  1.156650  2.763782             1.565174\n",
       "3  0.495355  1.374323             1.475889\n",
       "4  0.936218  2.763782             1.386604"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(data = x, columns = features).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#PCA Projection to 2D\n",
    "from sklearn.decomposition import PCA\n",
    "pca = PCA(n_components=2)\n",
    "principalComponents = pca.fit_transform(x)\n",
    "principalDf = pd.DataFrame(data = principalComponents\n",
    "             , columns = ['principal component 1', 'principal component 2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.970849</td>\n",
       "      <td>1.815191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.881608</td>\n",
       "      <td>1.215936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.335045</td>\n",
       "      <td>2.419418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.603524</td>\n",
       "      <td>1.266807</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.056962</td>\n",
       "      <td>2.470288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2\n",
       "0               3.970849               1.815191\n",
       "1               1.881608               1.215936\n",
       "2               2.335045               2.419418\n",
       "3               1.603524               1.266807\n",
       "4               2.056962               2.470288"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "principalDf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>HOSPITAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>INFANTA SOFÍA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>INFANTA SOFÍA</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          HOSPITAL\n",
       "0      SAN LLÁTZER\n",
       "19   INFANTA SOFÍA\n",
       "45     SAN LLÁTZER\n",
       "104    SAN LLÁTZER\n",
       "130  INFANTA SOFÍA"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['HOSPITAL']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>principal component 1</th>\n",
       "      <th>principal component 2</th>\n",
       "      <th>HOSPITAL</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.970849</td>\n",
       "      <td>1.815191</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.881608</td>\n",
       "      <td>1.215936</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.335045</td>\n",
       "      <td>2.419418</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.603524</td>\n",
       "      <td>1.266807</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2.056962</td>\n",
       "      <td>2.470288</td>\n",
       "      <td>SAN LLÁTZER</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   principal component 1  principal component 2     HOSPITAL\n",
       "0               3.970849               1.815191  SAN LLÁTZER\n",
       "1               1.881608               1.215936  SAN LLÁTZER\n",
       "2               2.335045               2.419418  SAN LLÁTZER\n",
       "3               1.603524               1.266807  SAN LLÁTZER\n",
       "4               2.056962               2.470288  SAN LLÁTZER"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "finalDf = pd.concat([principalDf, df[['HOSPITAL']]], axis = 1)\n",
    "finalDf.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ETk4Od999N8OGDcNkMgFqm9yysjL69+8fcxRn8ODBPPXUU+Tn50vQFg1CVvkSIoFNnjyZ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2Htmr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\nQogEIUFbCCGESBD/H7+WPZVATlGLAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e032bf438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Visualize 2D Projection\n",
    "fig = plt.figure(figsize = (8,8))\n",
    "ax = fig.add_subplot(1,1,1) \n",
    "ax.set_xlabel('Principal Component 1', fontsize = 15)\n",
    "ax.set_ylabel('Principal Component 2', fontsize = 15)\n",
    "ax.set_title('2 Component PCA', fontsize = 20)\n",
    "\n",
    "\n",
    "targets = ['SAN LLÁTZER', 'INFANTA SOFÍA']\n",
    "colors = ['r', 'g']\n",
    "for target, color in zip(targets,colors):\n",
    "    indicesToKeep = finalDf['HOSPITAL'] == target\n",
    "    ax.scatter(finalDf.loc[indicesToKeep, 'principal component 1']\n",
    "               , finalDf.loc[indicesToKeep, 'principal component 2']\n",
    "               , c = color\n",
    "               , s = 50)\n",
    "ax.legend(targets)\n",
    "ax.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.54647772,  0.34157262])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pca.explained_variance_ratio_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# KMeans Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x20e036fb668>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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b1oABAzR8+PAmLZsgBgCc0tztM2MevPV/61JqaqqWL18efH3zzTfHbF3cmgYA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\ng+PSlhWowZIS23Rs0H8sx31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      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e036fbac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Exploratory Data Analysis\n",
    "df['BURNOUT'] = df['BURNOUT'].replace([0, 1],['Yes', 'No'])\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "sns.lmplot('TIEMPO_VIDA_LABORAL','FUMADOR',data=df, hue='BURNOUT',\n",
    "           palette='coolwarm',size=6,aspect=1,fit_reg=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DW78F6wc/+EGS5Pbbb8+aNWt6Ctb8+fNz8sknZ+HChbnzzjsza9asAQ8LAAAwlPW77PSm\nN70pV199dZLkySefzCGHHJJ169Zl+vTpSZKZM2fm3nvvHdiUAAAAw0C/K1hJ0tzcnAULFuS//uu/\n8vnPfz4/+MEP0mg0kiQtLS3ZunXrbs9pbR2b5uamsmn306b/zTwUNYZgtubRQ+v7lwzcnPb3zx2K\ns0qGz3HVaDSG7AzrNFjfv319Hd+rshz/9epv/pMmjR/ENAeepqZRZlwj8x8Ye1WwkmTp0qW55JJL\nctZZZ6Wjo6Nn+7Zt2zJx4sTd9m9v79htW92qqqo7wh4NtXw7O7vqjrCbgZzR/vzZQ3FWydA7ppK+\nZ9U8umnIzrBOg/n925fX8r0qy/Ffr/7m39Y29H6fGUkmTRqftrZn645xwDL//XPooRP63N7vJYL/\n9m//lptvvjlJMm7cuDQajbz85S/PmjVrkiSrVq3KtGnTCkYFAAAYnvpdwXrzm9+cyy+/PO95z3uy\nc+fOXHHFFTnmmGNy1VVX5cYbb8zRRx+d2bNnD0ZWAACAIa3fgjV+/Ph87nOf2237ihUrBiQQAADA\ncLXX78ECntvxm++qOwIMmqF6vD80+Y11R2A/1XFsNRqNPb8HccrrBi8MMCL4dGAAAIBCFCwAAIBC\nFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwA\nAIBCFCwAAIBCFCwAAIBCFCwAAIBCFCwAAIBCmusOAADAvlm7cWzdEXbziikddUeAIcEKFgAAQCEK\nFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCHNdQdg\n6Dp+8111RwAAYJCs3Ti27gh9esWUjroj7BMrWAAAAIUoWAAAAIUoWAAAAIUoWAAAAIUoWAAAAIUo\nWAAAAIUoWAAAAIUoWAAAAIUoWAAAAIU01x0AOLAcv/mu3bY1Go1UVVVDGgCAsqxgAQAAFKJgAQAA\nFKJgAQAAFKJgAQAAFKJgAQAAFOIuggAwQPq6a+au6riL5kOT3ziorwdwILGCBQAAUIiCBQAAUMge\nLxHs7OzMFVdckQ0bNmTHjh350Ic+lGOPPTaXXXZZGo1GjjvuuCxatCijRulpAAAAeyxY3/3udzNp\n0qTccMMN+f3vf593vOMdOf744zN//vycfPLJWbhwYe68887MmjVrsPICAAAMWXssWG95y1sye/bs\nnsdNTU1Zt25dpk+fniSZOXNm7rnnnj4LVmvr2DQ3NxWOu382NRp1R3hOjSGc7UBg/vUy/3qNlPk3\njx5a/81J9m62dcx/KM4qqWcW/b3mpEnjBynJvmneVHeC3T2fWTU1jRqyMz4Q7Dr/oXhcJUP35/C5\n7LFgtbS0JEna29vz0Y9+NPPnz8/SpUt7TkYtLS3ZunVrn89tb+8oHHX/DfZdmvbVUM830pl/vcy/\nXiNh/js7u+qOsJu9netgz38oziqp7zjc0+u2tT07iEn23s7OsXVH2E1b277/7jdp0vghO+MDwa7z\nH4rHVfL8jq3BcOihE/rc3u+bpzZu3Jhzzz03Z5xxRk477bRe77fatm1bJk6cWC4lAADAMLbHgrVp\n06acd955+fjHP54zzzwzSXLCCSdkzZo1SZJVq1Zl2rRpA58SAABgGNhjwfryl7+cLVu25Kabbsq8\nefMyb968zJ8/P8uWLcucOXPS2dnZ6z1aAAAAB7I9vgfryiuvzJVXXrnb9hUrVgxYIAAAgOFqjwUL\nAIaL4zffVXcEOKCNe+RH+/ycrtFNGTfAN135fy1vGtA///l6xZSheeMG9p9PCAYAAChEwQIAAChE\nwQIAAChEwQIAACjETS4AAJ7D2o1j647ACDUUjq3mTcnOzvpzjDRWsAAAAApRsAAAAApRsAAAAApR\nsAAAAApRsAAAAApRsAAAAApRsAAAAApRsAAAAApRsAAAAApRsAAAAApprjsAADC4jt98V90RAEYs\nK1gAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgA\nAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACFKFgAAACF\nKFgAAACFKFgAAACFNNcdAAAADjTHb76r7ghpNBqpqqrn8UOT31hjmpHDChYAAEAhChYAAEAhChYA\nAEAhChYAAEAhbnIBAMB+29Kx7//fvrEjqaqB/f/9x3fUfzMJDixWsAAAAApRsAAAAArZq4L1s5/9\nLPPmzUuSrF+/PnPnzs0555yTRYsWpbu7e0ADAgAADBf9FqyvfOUrufLKK9PR0ZEkue666zJ//vzc\neuutqaoqd95554CHBAAAGA76LVhHHnlkli1b1vN43bp1mT59epJk5syZuffeewcuHQAAwDDS710E\nZ8+enSeeeKLncVVVaTQaSZKWlpZs3bq1z+e1to5Nc3NToZhlbPrf3ENRYwhnOxCYf73Mv17mXy/z\nr1d/828ePbR+lxnKns+x7Piv167zH6rH+6RJ4+uOsE/2+Tbto0b9ZdFr27ZtmThxYp/7tbd3PP9U\nA6Sqqroj7NFQzzfSmX+9zL9e5l8v86/Xnua/s7NrEJMMb8/3OHb81+v/zn+oHu9tbUOvVyTJoYdO\n6HP7Pt9F8IQTTsiaNWuSJKtWrcq0adP2LxkAAMAIsc8Fa8GCBVm2bFnmzJmTzs7OzJ49eyByAQAA\nDDt7dYng1KlTc8cddyRJjjrqqKxYsWJAQwEAAAxH+/weLACAA8Xxm++qO0KfHpr8xrojAM9hny8R\nBAAAoG8KFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAA\nQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCHNdQcAAADqd/zmu+qO0Lcpr6s7\nwT6xggUAAFCIggUAAFCIggUAAFCIggUAAFCIggUAAFCIggUAAFCIggUAAFCIggUAAFCIggUAAFCI\nggUAAFBIc90BAADYN8dvvqvuCMBzsIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQ\niIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIFAABQiIIF\nAABQiIIFAABQiIIFAABQSPPzeVJ3d3cWL16cX/ziFxkzZkyWLFmSF7/4xaWzAQAADCvPawXr+9//\nfnbs2JFvfvOb+ad/+qdcf/31pXMBAAAMO89rBesnP/lJXv/61ydJXvWqV+X+++/fbZ9DD52wf8kG\nwt++pe4EAADACPa8VrDa29vT2tra87ipqSk7d+4sFgoAAGA4el4Fq7W1Ndu2bet53N3dnebm57UY\nBgAAMGI8r4L16le/OqtWrUqS/M///E9e+tKXFg0FAAAwHDWqqqr29Ul/vovgL3/5y1RVlWuvvTbH\nHHPMQOQDAAAYNp5XwWL/vf3tb8+ECX+6EcjUqVMzZ86cXHPNNWlqasqMGTNy4YUX1pxw5Pr2t7+d\n73znO0mSjo6OPPjgg/nMZz6TT33qU5kyZUqS5CMf+UimT59eZ8wR6Wc/+1k+/elPZ/ny5Vm/fn0u\nu+yyNBqNHHfccVm0aFFGjRqVL3zhC/nhD3+Y5ubmXHHFFXnlK19Zd+wR4//O/8EHH8zVV1+dpqam\njBkzJkuXLs0hhxySJUuW5Kc//WlaWlqSJDfddFPPuYr983/nv27dulxwwQV5yUtekiSZO3du/v7v\n/97xP4D+7/wvuuiibNq0KUmyYcOGnHTSSfnsZz+bCy64IG1tbRk9enTGjh2br371qzWnHv46Oztz\nxRVXZMOGDdmxY0c+9KEP5dhjj3X+HyR9zf/www93/h9oFYPuj3/8Y3XGGWf02nb66adX69evr7q7\nu6v3v//91f33319TugPL4sWLq9tvv7268cYbq+9973t1xxnRbrnlluptb3tb9e53v7uqqqr64Ac/\nWP34xz+uqqqqrrrqquo///M/q/vvv7+aN29e1d3dXW3YsKF65zvfWWfkEWXX+b/nPe+pHnjggaqq\nquq2226rrr322qqqqurss8+unnnmmdpyjlS7zv+OO+6ovva1r/Xax/E/cHad/5+1tbVVp59+evX0\n009XVVVVb33rW6vu7u46Io5YK1eurJYsWVJVVVVt3ry5OvXUU53/B1Ff83f+H3jP6z1Y7J+HHnoo\n27dvz3nnnZdzzz039913X3bs2JEjjzwyjUYjM2bMyOrVq+uOOeKtXbs2jzzySObMmZN169blW9/6\nVs4555xcf/317oo5AI488sgsW7as5/G6det6VglnzpyZe++9Nz/5yU8yY8aMNBqNHH744enq6srm\nzZvrijyi7Dr/G2+8MS972cuSJF1dXRk7dmy6u7uzfv36LFy4MGeffXZWrlxZV9wRZ9f533///fnh\nD3+Y97znPbniiivS3t7u+B9Au87/z5YtW5b3vve9Oeyww7Jp06Zs2bIlF1xwQebOnZsf/OAHNSQd\ned7ylrfkYx/7WM/jpqYm5/9B1Nf8nf8HnoJVg4MOOijnn39+vva1r+UTn/hELr/88owbN67n6y0t\nLdm6dWuNCQ8MN998cz784Q8nSV73utflqquuyje+8Y08++yzuf3222tON/LMnj27191Gq6pKo9FI\n8pdjftePgPCzUM6u8z/ssMOSJD/96U+zYsWKvO9978uzzz6b9773vbnhhhvy1a9+Nbfeemseeuih\nuiKPKLvO/5WvfGUuvfTSfOMb38gRRxyRL37xi47/AbTr/JPkmWeeyerVq/POd74zyZ8upTrvvPPy\nxS9+MV/4whdy3XXX5Zlnnqkj7ojS0tKS1tbWtLe356Mf/Wjmz5/v/D+I+pq/8//AU7BqcNRRR+X0\n009Po9HIUUcdlQkTJqStra3n69u2bcvEiRNrTDjybdmyJb/+9a9zyimnJEne9a535Ygjjkij0cjf\n/d3f5YEHHqg54cg3atRfTj9/PuZ3/QiIbdu2uf57AP3Hf/xHFi1alFtuuSWTJ0/OuHHjcu6552bc\nuHFpbW3NKaec4j+wA2TWrFl5+ctf3vPvDzzwgON/kH3ve9/L2972tjQ1NSVJDjnkkJx99tlpbm7O\nC1/4wrzsZS/Lo48+WnPKkWHjxo0599xzc8YZZ+S0005z/h9ku84/cf4faApWDVauXJnrr78+SfL0\n009n+/btGT9+fB577LFUVZW7774706ZNqznlyHbfffflta99bZI/raScfvrpeeqpp5Ikq1evzokn\nnlhnvAPCCSeckDVr1iRJVq1alWnTpuXVr3517r777nR3d+fJJ59Md3d3Jk+eXHPSkenf//3fs2LF\niixfvjxHHHFEkuQ3v/lNzjnnnHR1daWzszM//elP/SwMkPPPPz8///nPk/zlnOP4H1yrV6/OzJkz\nex7fe++9mT9/fpI//XL/8MMP5+ijj64r3oixadOmnHfeefn4xz+eM888M4nz/2Dqa/7O/wPPpwPX\n4Mwzz8zll1+euXPnptFo5Nprr82oUaNyySWXpKurKzNmzMhJJ51Ud8wR7dFHH83UqVOTJI1GI0uW\nLMmFF16Ygw46KMccc0zOOuusmhOOfAsWLMhVV12VG2+8MUcffXRmz56dpqamTJs2LXPmzEl3d3cW\nLlxYd8wRqaurK9dcc02mTJmSj3zkI0mS17zmNfnoRz+a0047LWeddVZGjx6dM844I8cdd1zNaUem\nxYsX5+qrr87o0aNzyCGH5Oqrr05ra6vjfxA9+uijPb9cJsmpp56au+++O2eddVZGjRqViy++2C/4\nBXz5y1/Oli1bctNNN+Wmm25KkvzzP/9zlixZ4vw/CHadf1dXVx5++OEcfvjhzv8DyG3aAQAACnGJ\nIAAAQCEKFgAAQCEKFgAAQCEKFgAAQCEKFgAAQCFu0w5An9asWZP58+fn2GOP7dn2ghe8IJ///Od7\nHj/xxBO5+OKLc8cdd2TevHnZvn17xo0b1/P1888/P8cee2xOP/30ns9U6ejoyPjx4/O5z30uBx98\ncP7whz9k6dKlWb9+fbq6ujJlypR88pOfzIQJEzJv3rwsXrw4xxxzTB5++OHccMMN2b59e5599tmc\neuqp+chHPpJGo5HXve51uedPoH7gAAAFG0lEQVSee5IkP/7xj3PTTTelqqp0dnZm9uzZed/73pdG\no9Hr7/fGN74xU6ZM6fnQ04MPPjhf+MIXer3mLbfcknvvvTejRo1Ko9HIRRddlJe//OVZtmxZDjnk\nkMydO3eP+Ts7O3PzzTfn3nvvTVNTU5qbmzN//nwfxQEwgilYADynU045JZ/97Gf3ev+lS5fmmGOO\n6bXtiSeeyLHHHpvly5f3bPvMZz6TlStX5vzzz8/FF1+cs88+O7NmzUqS/Mu//EsWLlzY63W3bNmS\niy++OMuWLctLXvKSdHV15WMf+1huv/32zJ07t2e/hx9+OEuXLs3NN9+cww47LDt37szixYvzta99\nLe9///t3y/v1r389Y8eO7fPv8sgjj+Suu+7KbbfdlkajkQcffDALFizId7/73V777Sn/5z//+XR1\ndWXFihUZNWpUNmzYkA9+8IP50pe+1OszmAAYOVwiCMCgqqoqGzduzMSJE7Nhw4Zs2rSpp5wkybx5\n8/LJT36y13PuvPPOnHzyyXnJS16SJGlqasrSpUvzrne9q9d+t912Wz74wQ/msMMOS5I0Nzfnsssu\nyze/+c19zjl58uQ8+eSTWblyZZ5++um87GUvy8qVK3vt01/+7373u7n44ot7Vsn+6q/+Kuecc06+\n853v7HMeAIYHK1gAPKcf//jHmTdvXs/jU089tc+VoD9bsGBBr0sEP/e5zyX502rQvHnz0tbWlo6O\njpx22ml5xzvekbVr12bq1Km9/oympqZMmDCh17bf/va3u634tLS07Pb6jz/+eM4888xe21pbW7N9\n+/Z0d3f3FJ0/O++883q2nX/++fnbv/3bnq9Nnjw5X/rSl7JixYp88YtfzEEHHZSLLroos2fP7pXr\nufI/88wzOfjgg9Pc3Ps/tUcccUR+/vOf75YdgJFBwQLgOT3XJYJ/+MMfcvDBBydJr/c29XWJ4LPP\nPttzieAf//jHXHDBBXnhC1+Y5ubmHH744Xnqqad67d/Z2Znvfe97Oe2003q2HX744XnggQd67ff4\n44/nqaeeymte85qebS960YuyYcOGnHDCCT3b2tvbM2bMmN3KVbLnSwTXr1+f1tbWXHfddUmStWvX\n5gMf+EBOPvnkXrmeK//s2bPzhz/8ITt37uxVstavX58pU6b0+ZoADH8uEQRgn7S3t+cd73hHqqrK\nb3/720yePHmvn3vQQQfl05/+dG666aY89NBDedGLXpQXvOAF+f73v9+zz7/+67/2epwkb3jDG/Kj\nH/0ojz32WJI/lZjrr78+v/zlL3vtN3fu3HzpS1/K7373u579rrnmmpx99tn7/Pf8xS9+kcWLF6ej\noyNJctRRR2XChAlpamrq2WdP+ceMGZO3vvWt+exnP5vu7u4kfyqFt956a975znfucx4AhgcrWAA8\np10vEUySr3zlKznttNNy1llnZfTo0Vm4cGHP13a9RPCtb31rZs6c2ev5hxxySC699NIsXLgwt99+\nez71qU/lk5/8ZL7+9a+ns7MzRx55ZJYsWdLrOa2trbn++utz5ZVXpqqqbNu2LW94wxtyzjnn9Nrv\nxBNPzEUXXZSLLrooXV1d2blzZ2bNmrXHyxqfy5vf/Ob86le/yrvf/e6MHz8+VVXl0ksv3e3yxT3l\nv+SSS7Js2bKeWY0ZMyZLlixxgwuAEaxRVVVVdwgAAICRwCWCAAAAhShYAAAAhShYAAAAhShYAAAA\nhShYAAAAhShYAAAAhShYAAAAhfx/S/yCMrb392MAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e0369a438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"BURNOUT\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'EJERCICIO FISICO',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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eeuqpjB8/PpVKJSNGjEhHR0fWrVvXc1MDAADUocZdHTBhwoS88sorXR9Xq9VUKpUkyeDB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\n/r/AAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02fe3588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"BURNOUT\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'HORAS_DE_SUEÑO',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gEAULAACgEAULAACgEAULAACgEAULAACgEAULAACgEAULAACgEAULAACgEAULAACgEAUL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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e04d44d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"BURNOUT\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'PESO',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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xZsfa0ffqzI4FANAWO1gAAACJKFgAAACJKFgAAACJKFgAAACJKFgAAACJKFgAAACJKFgA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\nJKJgAQAAJPL/AALma/1QbfdfAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e032bc8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"BURNOUT\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'ALTURA',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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gEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACF1Js9wGR6ZbB6PVmrNXsCAKiGM166\nr9kjTBkT+VrVarU0Go3DftwTx54/AdPA1FO94gAAAJiiBBYAAEAhAgsAAKAQgQUAAFCIwAIAAChE\nYAEAABQisAAAAAoRWAAAAIUILAAAgEIEFgAAQCECCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAA\nChFYAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUUm/2AABMLWe8dF+zRxjV\nE8ee3+wRAKa00sf3owf2F3me3actKvI8k8UZLAAAgEIEFgAAQCECCwAAoBCBBQAAUIhNLqCFNXsz\nglqtlkaj0dQZAAAmkzNYAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAA\nAAAKOegHDQ8NDeULX/hC/v73v2fPnj25+uqrc9ppp2XVqlWp1WqZN29e1q1bl7Y2nQYAAHDQwPrR\nj36Urq6u3HLLLXn55ZfzoQ99KGeccUa6u7tz7rnnZu3atdm6dWve/e53T9a8AAAAlXXQU0/vfe97\n8+lPf3r46/b29vT29mbBggVJksWLF2f79u0TOyEAAMAUcdAzWJ2dnUmS/v7+fOpTn0p3d3c2bNiQ\nWq02fP+uXbtGfezMmdNTr7cXHve1eeFfc1dJrVZLfVq1Xqd/q1Xw9arqa1VVzX4Pm/3rH44qrq2p\n9PpVwWS+h4d67K7qe1jF9Z5U9/U60oz3fajquqqqef/zi2aPMFLhP4Ol1kRXV0eR52lvbyv2XAdz\n0MBKkmeffTbXXHNNLrvssnzgAx/ILbfcMnzfwMBAZs+ePerj+vsHy01ZSKPRaPYIo9o7tK/ZI4yq\niq9XVV+rqqrCe1iFGQ5FFdfWVHntqmIy38P6tPZD+vWq+h5Wcb0n1X29jkTjeS+quq6q6khY73uH\n9hd5nl19rxZ5nq6ujvQVeq4kmTNn1qi3H/QSwRdeeCFXXHFFPve5z+Xiiy9OkrzpTW9KT09PkmTb\ntm2ZP39+sSEBAACmsoMG1q233ppXXnkl3/zmN7NixYqsWLEi3d3d2bhxY5YtW5ahoaEsXbp0smYF\nAACotINeIrhmzZqsWbNmxO133HHHhA0EAAAwVfkAKwAAgEIEFgAAQCECCwAAoBCBBQAAUIjAAgAA\nKERgAQAAFHLQbdoBYKo446X7Ju3XqtVqaTQak/brAeM3mccGSJzBAgAAKEZgAQAAFCKwAAAAChFY\nAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKqTd7ADgcZ7x0X7NH\nGNUTx57f7BEADllVj6UArcAZLAAAgEIEFgAAQCECCwAAoBCBBQAAUIhNLgDih/4BgDKcwQIAAChE\nYAEAABQisAAAAAoRWAAAAIUILAAAgELsIggF2IEOAIDEGSwAAIBiBBYAAEAhAgsAAKAQgQUAAFCI\nwAIAAChEYAEAABQisAAAAAoRWAAAAIUILAAAgEIEFgAAQCECCwAAoJB6swcgOeOl+5o9AgAAUIAz\nWAAAAIUILAAAgEIEFgAAQCECCwAAoBCbXAAA8JrZtAv+yRksAACAQgQWAABAIYcUWI8++mhWrFiR\nJHnqqaeyfPnyXHbZZVm3bl32798/oQMCAABMFWMG1u233541a9ZkcHAwSbJ+/fp0d3dny5YtaTQa\n2bp164QPCQAAMBWMGVgnnXRSNm7cOPx1b29vFixYkCRZvHhxtm/fPnHTAQAATCFj7iK4dOnSPPPM\nM8NfNxqN1Gq1JElnZ2d27do16uNmzpyeer290JhlvPCvuaukVsGZoBTrm1ZlbdPKrG9KqU8r0wJd\nXR1Fnqe9va3Ycx3MYW/T3tb2n5NeAwMDmT179qjf198/OP6pJkij0Wj2CKOq6lxQgvVNq7K2aWXW\nNyXsHSqzV8OuvleLPE9XV0f6Cj1XksyZM2vU2w97F8E3velN6enpSZJs27Yt8+fPf22TAQAAtIjD\nDqyVK1dm48aNWbZsWYaGhrJ06dKJmAsAAGDKqTUm6BzwP/4x+s9mNdNQ70PNHmGEWq3mNDwty/qm\nVVnbtDLrm1JmTy9zieDu0xYVeZ7KXiIIAADA6AQWAABAIQILAACgEIEFAABQiMACAAAoRGABAAAU\nIrAAAAAKEVgAAACFCCwAAIBCBBYAAEAhAgsAAKAQgQUAAFCIwAIAAChEYAEAABQisAAAAAoRWAAA\nAIUILAAAgEIEFgAAQCECCwAAoBCBBQAAUIjAAgAAKERgAQAAFCKwAAAAChFYAAAAhQgsAACAQgQW\nAABAIQILAACgEIEFAABQiMACAAAoRGABAAAUIrAAAAAKEVgAAACFCCwAAIBCBBYAAEAhAgsAAKAQ\ngQUAAFCIwAIAAChEYAEAABQisAAAAAoRWAAAAIUILAAAgEIEFgAAQCECCwAAoBCBBQAAUIjAAgAA\nKERgAQAAFCKwAAAAChFYAAAAhQgsAACAQgQWAABAIfXxPGj//v25/vrr84c//CFHHXVUbrjhhpx8\n8smlZwMAAJhSxnUG6xe/+EX27NmTu+66K5/97Gdz8803l54LAABgyhnXGaxHHnkkixYtSpK85S1v\nye9///sR3zNnzqzXNtlEWPLeZk8AAAAchpkFn2syGmVcZ7D6+/szc+Z/fqvt7e3Zu3dvsaEAAACm\nonEF1syZMzMwMDD89f79+1Ovj+tkGAAAQMsYV2C99a1vzbZt25Ikv/vd73L66acXHQoAAGAqqjUa\njcbhPujfuwj+8Y9/TKPRyE033ZRTTz11IuYDAACYMsYVWIzPvn37smbNmvzlL39Je3t71q9fn0aj\nkVWrVqVWq2XevHlZt25d2tp8PBlT04svvpiLLroo3/nOd1Kv161tWsaFF16YWbP++YPRJ5xwQpYt\nW5Ybb7wx7e3tWbhwYT75yU82eUIYv02bNuW+++7L0NBQli9fngULFjh+M+Xde++9+cEPfpAkGRwc\nzOOPP57NmzdPyrHbn5ZJdP/99ydJvve97+VTn/pU1q9fn/Xr16e7uztbtmxJo9HI1q1bmzwljM/Q\n0FDWrl2bGTNmJIm1TcsYHBxMkmzevDmbN2/O+vXrs27dunz5y1/OnXfemUcffTS9vb1NnhLGp6en\nJ7/97W9z5513ZvPmzXnuueccv2kJF1100fBx+8wzz8yaNWsm7dgtsCbRBRdckC9+8YtJkp07d+b1\nr399ent7s2DBgiTJ4sWLs3379maOCOO2YcOGXHrppTnuuOOSxNqmZTzxxBPZvXt3rrjiilx++eV5\n+OGHs2fPnpx00kmp1WpZuHBhduzY0ewxYVwefPDBnH766bnmmmty1VVXZcmSJY7ftJTHHnssTz75\nZN7//vdP2rHb1n+TrF6vZ+XKlfn5z3+eb3zjG7n//vtTq9WSJJ2dndm1a1eTJ4TDd++99+bYY4/N\nokWLcttttyVJGo2GtU1LmDFjRj7+8Y/nkksuyV//+tdceeWVmT179vD9nZ2defrpp5s4IYzfyy+/\nnJ07d+bWW2/NM888k6uvvtrxm5ayadOmXHPNNSM+Zmoij90Cqwk2bNiQa6+9Nh/+8IeHLz1JkoGB\ngQP+0oap4p577kmtVsuOHTvy+OOPZ+XKlXnppZeG77e2mcpOOeWUnHzyyanVajnllFMya9as9PX1\nDd9vfTOVdXV1Ze7cuTnqqKMyd+7cTJ8+Pc8999zw/dY3U9krr7ySP//5zznvvPPS399/wMdMTeTa\ndongJPrhD3+YTZs2JUmOPvro1Gq1nHXWWenp6UmSbNu2LfPnz2/miDAu3/3ud3PHHXdk8+bNeeMb\n35gNGzZk8eLF1jYt4e67787NN9+cJHn++eeze/fudHR05G9/+1sajUYefPBB65sp65xzzskDDzyQ\nRqMxvL7f9ra3OX7TEh5++OG8/e1vT/LPz/GdNm3apBy77SI4iV599dWsXr06L7zwQvbu3Zsrr7wy\np556aq677roMDQ1l7ty5ueGGG9Le3t7sUWHcVqxYkeuvvz5tbW3WNi1hz549Wb16dXbu3JlarZZr\nr702bW1tuemmm7Jv374sXLgwn/nMZ5o9Jozbl770pfT09KTRaOQzn/lMTjjhBMdvWsK3v/3t1Ov1\nfPSjH03yz8/vnYxjt8ACAAAoxCWCAAAAhQgsAACAQgQWAABAIQILAACgEIEFAABQiA8aBqByenp6\n0t3dndNOO234tmOOOSYdHR3p7e1NV1dX9u7dm2OOOSarV6/OiSeeOPx9V111VZLk1ltvHb7t/PPP\nz/HHH5+2trYMDg7mzDPPzKpVqzJ9+vTJ+00BcEQQWABU0nnnnZevfvWrB9y2atWqfO5zn8vixYuT\nJL/+9a/T3d2de+65J0ny7LPP5tVXX83Q0FCefvrpA8LrO9/5znBQfetb38pXv/rVrFq1apJ+NwAc\nKVwiCMCUNX/+/EybNi1PPfVUkuTuu+/Ou971rlx44YXZsmXLf33cxz72sfzsZz+brDEBOII4gwVA\nJf3qV7/KihUrhr9+xzveMer3ve51r8vLL7+cE088MT/5yU9y1113pV6v5/3vf38+/elPZ8aMGSMe\nM2PGjAwODk7Y7AAcuQQWAJX03y4R/L927tyZN7zhDXnggQcyMDCQz372s0mS/fv358c//nEuueSS\nEY/p7+9PZ2fnxAwOwBFNYAEwZT300EOZMWNG3vCGN+TGG2/MDTfckCVLliRJHnnkkdxwww2jBtbt\nt9+e973vfZM8LQBHAoEFQCX930sEk39eDnjLLbfk9ttvT1tbWzo7O/O1r30tL774Yh599NEDznid\nc845GRwczG9+85skyRVXXJG2trbs378/b3zjG/P5z39+Un8/ABwZao1Go9HsIQAAAFqBXQQBAAAK\nEVgAAACFCCwAAIBCBBYAAEAhAgsAAKAQgQUAAFCIwAIAACjk/wMDdW9xu9W5JAAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x20e02f85128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style('darkgrid')\n",
    "g = sns.FacetGrid(df,hue=\"BURNOUT\",palette='coolwarm',size=6,aspect=2)\n",
    "g = g.map(plt.hist,'EDAD',bins=20,alpha=0.7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(algorithm='auto', copy_x=True, init='k-means++', max_iter=300,\n",
       "    n_clusters=2, n_init=10, n_jobs=1, precompute_distances='auto',\n",
       "    random_state=None, tol=0.0001, verbose=0)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Kmeans cluster creation\n",
    "from sklearn.cluster import KMeans\n",
    "#Define number of clusters\n",
    "kmeans = KMeans(n_clusters=2)\n",
    "df['HOSPITAL'] = df['HOSPITAL'].replace(['SAN LLÁTZER', 'INFANTA SOFÍA'],[1, 2])\n",
    "df.rename(columns={'BURNOUT':'TBURNOUT'}, inplace=True)\n",
    "#Fit all the model less TBURNOUT column\n",
    "kmeans.fit(df.drop('TBURNOUT',axis=1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.01200000e+03,   2.22044605e-16,   4.88835556e+01,\n",
       "          6.22222222e-01,   1.80148148e+00,   1.63264148e+02,\n",
       "          6.34518519e+01,   7.03200000e+00,   1.18767407e+02,\n",
       "          5.08148148e-01,  -3.27600000e+01,   1.57792593e+01,\n",
       "          1.80740741e-01,   5.86666667e-01,   1.71407407e+00],\n",
       "       [  3.37500000e+02,   9.62908012e-01,   4.61854599e+01,\n",
       "          6.17210682e-01,   1.63353116e+00,   1.62217062e+02,\n",
       "          8.90464180e+01,   6.51735905e+00,   1.20553412e+02,\n",
       "          4.64391691e-01,  -2.73562315e+01,   1.83130564e+01,\n",
       "          1.95845697e-01,   7.13649852e-01,   1.68991098e+00]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Guessing cluster center vectors\n",
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>USUARIO</th>\n",
       "      <th>SEXO</th>\n",
       "      <th>EDAD</th>\n",
       "      <th>ESTADO_CIVIL</th>\n",
       "      <th>NUMERO_HIJOS</th>\n",
       "      <th>ALTURA</th>\n",
       "      <th>PESO</th>\n",
       "      <th>HORAS_DE_SUEÑO</th>\n",
       "      <th>EJERCICIO FISICO</th>\n",
       "      <th>LECTURA</th>\n",
       "      <th>TIEMPO_VIDA_LABORAL</th>\n",
       "      <th>ESPECIALIDAD</th>\n",
       "      <th>FUMADOR</th>\n",
       "      <th>VIVE_EN_PAREJA</th>\n",
       "      <th>TBURNOUT</th>\n",
       "      <th>HOSPITAL</th>\n",
       "      <th>Cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>28.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>181.80</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>9.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>166.80</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>8.5</td>\n",
       "      <td>59.0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>15</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>30.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>169.30</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>1</td>\n",
       "      <td>5.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>31.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.55</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>7.3</td>\n",
       "      <td>144.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>28</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>168.05</td>\n",
       "      <td>120.000000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>156</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>33.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>165.00</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>196.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>182</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>167.00</td>\n",
       "      <td>98.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>222.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-3.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>232</th>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-5.0</td>\n",
       "      <td>11</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>163.00</td>\n",
       "      <td>80.257143</td>\n",
       "      <td>7.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>290</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>165.00</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.4</td>\n",
       "      <td>133.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-9.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>319</th>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>167.00</td>\n",
       "      <td>79.714286</td>\n",
       "      <td>6.2</td>\n",
       "      <td>162.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-11.0</td>\n",
       "      <td>16</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>347</th>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>39.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>162.00</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-13.0</td>\n",
       "      <td>29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>40.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>164.00</td>\n",
       "      <td>79.171429</td>\n",
       "      <td>6.8</td>\n",
       "      <td>219.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-15.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165.00</td>\n",
       "      <td>80.800000</td>\n",
       "      <td>6.8</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-17.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>449</th>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>166.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.8</td>\n",
       "      <td>95.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-19.0</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>485</th>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>167.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.4</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-21.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>161.00</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>6.4</td>\n",
       "      <td>168.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-23.0</td>\n",
       "      <td>23</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>558</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>162.00</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>204.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>46.0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>163.00</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>6.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-27.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>631</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>164.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.6</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-29.0</td>\n",
       "      <td>23</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>668</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>165.00</td>\n",
       "      <td>82.428571</td>\n",
       "      <td>6.6</td>\n",
       "      <td>117.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-31.0</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>702</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>166.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>151.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>736</th>\n",
       "      <td>23</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>167.00</td>\n",
       "      <td>96.000000</td>\n",
       "      <td>6.2</td>\n",
       "      <td>47.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-35.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>24</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>161.00</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>6.8</td>\n",
       "      <td>81.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-37.0</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>816</th>\n",
       "      <td>25</td>\n",
       "      <td>1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>162.00</td>\n",
       "      <td>84.600000</td>\n",
       "      <td>5.8</td>\n",
       "      <td>127.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-39.0</td>\n",
       "      <td>11</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>854</th>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>53.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>164.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>5.7</td>\n",
       "      <td>165.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-41.0</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>892</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>166.00</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>5.6</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-43.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>161.00</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>6.7</td>\n",
       "      <td>103.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>968</th>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>161.00</td>\n",
       "      <td>89.000000</td>\n",
       "      <td>6.5</td>\n",
       "      <td>141.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-47.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1017</th>\n",
       "      <td>30</td>\n",
       "      <td>1</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>151.00</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>5.3</td>\n",
       "      <td>52.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-49.0</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>288</th>\n",
       "      <td>1320</td>\n",
       "      <td>0</td>\n",
       "      <td>36.3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>161.80</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>7.3</td>\n",
       "      <td>131.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-7.6</td>\n",
       "      <td>14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>289</th>\n",
       "      <td>1321</td>\n",
       "      <td>0</td>\n",
       "      <td>36.7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>160.55</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-8.4</td>\n",
       "      <td>15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>1322</td>\n",
       "      <td>0</td>\n",
       "      <td>37.1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>159.30</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>159.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-9.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>317</th>\n",
       "      <td>1323</td>\n",
       "      <td>0</td>\n",
       "      <td>37.5</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>158.05</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>318</th>\n",
       "      <td>1324</td>\n",
       "      <td>0</td>\n",
       "      <td>37.9</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>156.80</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>161.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-10.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>1325</td>\n",
       "      <td>0</td>\n",
       "      <td>38.3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>167.00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>188.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-11.6</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>346</th>\n",
       "      <td>1326</td>\n",
       "      <td>0</td>\n",
       "      <td>38.7</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>166.00</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>189.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-12.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>1327</td>\n",
       "      <td>0</td>\n",
       "      <td>39.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>165.00</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-13.2</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>374</th>\n",
       "      <td>1328</td>\n",
       "      <td>0</td>\n",
       "      <td>39.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>164.00</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>217.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-14.0</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>375</th>\n",
       "      <td>1329</td>\n",
       "      <td>0</td>\n",
       "      <td>39.9</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>163.00</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>218.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-14.8</td>\n",
       "      <td>21</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>410</th>\n",
       "      <td>1330</td>\n",
       "      <td>0</td>\n",
       "      <td>40.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>162.00</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>56.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-15.6</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1331</td>\n",
       "      <td>0</td>\n",
       "      <td>40.7</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>161.00</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>57.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-16.4</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>446</th>\n",
       "      <td>1332</td>\n",
       "      <td>0</td>\n",
       "      <td>41.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>167.00</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>92.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-17.2</td>\n",
       "      <td>26</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>447</th>\n",
       "      <td>1333</td>\n",
       "      <td>0</td>\n",
       "      <td>41.5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>166.00</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>93.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-18.0</td>\n",
       "      <td>36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>448</th>\n",
       "      <td>1334</td>\n",
       "      <td>0</td>\n",
       "      <td>41.9</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>165.00</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-18.8</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>483</th>\n",
       "      <td>1335</td>\n",
       "      <td>0</td>\n",
       "      <td>42.3</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>164.00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>129.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-19.6</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>484</th>\n",
       "      <td>1336</td>\n",
       "      <td>0</td>\n",
       "      <td>42.7</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>163.00</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>130.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-20.4</td>\n",
       "      <td>20</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>1337</td>\n",
       "      <td>0</td>\n",
       "      <td>43.1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>162.00</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>8.3</td>\n",
       "      <td>165.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-21.2</td>\n",
       "      <td>39</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>1338</td>\n",
       "      <td>0</td>\n",
       "      <td>43.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>161.00</td>\n",
       "      <td>58.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>166.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-22.0</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>1339</td>\n",
       "      <td>0</td>\n",
       "      <td>43.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>167.00</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>9.0</td>\n",
       "      <td>167.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-22.8</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>556</th>\n",
       "      <td>1340</td>\n",
       "      <td>0</td>\n",
       "      <td>44.3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>166.00</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>202.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-23.6</td>\n",
       "      <td>17</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>557</th>\n",
       "      <td>1341</td>\n",
       "      <td>0</td>\n",
       "      <td>44.7</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>165.00</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>8.0</td>\n",
       "      <td>203.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-24.4</td>\n",
       "      <td>18</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>592</th>\n",
       "      <td>1342</td>\n",
       "      <td>0</td>\n",
       "      <td>45.1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>164.00</td>\n",
       "      <td>64.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-25.2</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>593</th>\n",
       "      <td>1343</td>\n",
       "      <td>0</td>\n",
       "      <td>45.5</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>163.00</td>\n",
       "      <td>65.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-26.0</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>594</th>\n",
       "      <td>1344</td>\n",
       "      <td>0</td>\n",
       "      <td>45.9</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>162.00</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-26.8</td>\n",
       "      <td>6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>629</th>\n",
       "      <td>1345</td>\n",
       "      <td>0</td>\n",
       "      <td>46.3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>161.00</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-27.6</td>\n",
       "      <td>18</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>630</th>\n",
       "      <td>1346</td>\n",
       "      <td>0</td>\n",
       "      <td>46.7</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>167.00</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>1</td>\n",
       "      <td>-28.4</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1</td>\n",
       "      <td>Yes</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>1347</td>\n",
       "      <td>0</td>\n",
       "      <td>47.1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>166.00</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>7.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-29.2</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>666</th>\n",
       "      <td>1348</td>\n",
       "      <td>0</td>\n",
       "      <td>47.5</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>165.00</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>7.5</td>\n",
       "      <td>115.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-30.0</td>\n",
       "      <td>19</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>667</th>\n",
       "      <td>1349</td>\n",
       "      <td>0</td>\n",
       "      <td>47.9</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>164.00</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>7.4</td>\n",
       "      <td>116.0</td>\n",
       "      <td>0</td>\n",
       "      <td>-30.8</td>\n",
       "      <td>20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>No</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1349 rows × 17 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      USUARIO  SEXO  EDAD  ESTADO_CIVIL  NUMERO_HIJOS  ALTURA        PESO  \\\n",
       "0           1     1  28.0             1             0  181.80  120.000000   \n",
       "19          2     1  29.0             0             0  166.80   98.000000   \n",
       "45          3     1  30.0             1             0  169.30  120.000000   \n",
       "104         4     1  31.0             1             0  165.55   98.000000   \n",
       "130         5     1  32.0             1             0  168.05  120.000000   \n",
       "156         6     1  33.0             1             0  165.00   96.000000   \n",
       "182         7     1  34.0             0             0  167.00   98.000000   \n",
       "232         8     1  35.0             0             0  161.00   87.000000   \n",
       "262         9     1  36.0             0             0  163.00   80.257143   \n",
       "290        10     1  37.0             0             0  165.00   86.000000   \n",
       "319        11     1  38.0             1             0  167.00   79.714286   \n",
       "347        12     1  39.0             1             0  162.00   86.000000   \n",
       "376        13     1  40.0             1             1  164.00   79.171429   \n",
       "412        14     1  41.0             1             1  165.00   80.800000   \n",
       "449        15     1  42.0             1             1  166.00   87.000000   \n",
       "485        16     1  43.0             1             2  167.00   87.000000   \n",
       "522        17     1  44.0             1             2  161.00   88.000000   \n",
       "558        18     1  45.0             1             2  162.00   88.000000   \n",
       "595        19     1  46.0             1             2  163.00   89.000000   \n",
       "631        20     1  47.0             1             3  164.00   87.000000   \n",
       "668        21     1  48.0             1             3  165.00   82.428571   \n",
       "702        22     1  49.0             1             3  166.00   87.000000   \n",
       "736        23     1  50.0             0             3  167.00   96.000000   \n",
       "770        24     1  51.0             0             4  161.00   83.000000   \n",
       "816        25     1  52.0             0             2  162.00   84.600000   \n",
       "854        26     1  53.0             0             2  164.00   87.000000   \n",
       "892        27     1  54.0             0             2  166.00   87.000000   \n",
       "930        28     1  55.0             0             2  161.00   83.000000   \n",
       "968        29     1  56.0             0             3  161.00   89.000000   \n",
       "1017       30     1  57.0             0             3  151.00   90.000000   \n",
       "...       ...   ...   ...           ...           ...     ...         ...   \n",
       "288      1320     0  36.3             0             0  161.80   64.000000   \n",
       "289      1321     0  36.7             0             0  160.55   65.000000   \n",
       "316      1322     0  37.1             0             0  159.30   61.000000   \n",
       "317      1323     0  37.5             1             0  158.05   62.000000   \n",
       "318      1324     0  37.9             1             0  156.80   63.000000   \n",
       "345      1325     0  38.3             1             0  167.00   60.000000   \n",
       "346      1326     0  38.7             1             0  166.00   61.000000   \n",
       "373      1327     0  39.1             1             1  165.00   57.000000   \n",
       "374      1328     0  39.5             0             1  164.00   58.000000   \n",
       "375      1329     0  39.9             1             1  163.00   59.000000   \n",
       "410      1330     0  40.3             1             1  162.00   64.000000   \n",
       "411      1331     0  40.7             1             1  161.00   57.000000   \n",
       "446      1332     0  41.1             1             1  167.00   61.000000   \n",
       "447      1333     0  41.5             0             1  166.00   62.000000   \n",
       "448      1334     0  41.9             0             1  165.00   63.000000   \n",
       "483      1335     0  42.3             0             2  164.00   60.000000   \n",
       "484      1336     0  42.7             0             2  163.00   61.000000   \n",
       "519      1337     0  43.1             1             2  162.00   57.000000   \n",
       "520      1338     0  43.5             1             2  161.00   58.000000   \n",
       "521      1339     0  43.9             1             2  167.00   59.000000   \n",
       "556      1340     0  44.3             1             2  166.00   66.000000   \n",
       "557      1341     0  44.7             1             2  165.00   67.000000   \n",
       "592      1342     0  45.1             1             2  164.00   64.000000   \n",
       "593      1343     0  45.5             1             2  163.00   65.000000   \n",
       "594      1344     0  45.9             1             2  162.00   66.000000   \n",
       "629      1345     0  46.3             1             3  161.00   62.000000   \n",
       "630      1346     0  46.7             1             3  167.00   63.000000   \n",
       "665      1347     0  47.1             1             3  166.00   60.000000   \n",
       "666      1348     0  47.5             1             3  165.00   61.000000   \n",
       "667      1349     0  47.9             0             3  164.00   62.000000   \n",
       "\n",
       "      HORAS_DE_SUEÑO  EJERCICIO FISICO  LECTURA  TIEMPO_VIDA_LABORAL  \\\n",
       "0                9.0              40.0        1                  9.0   \n",
       "19               8.5              59.0        0                  7.0   \n",
       "45               9.0              85.0        1                  5.0   \n",
       "104              7.3             144.0        1                  3.0   \n",
       "130              7.4             170.0        1                  1.0   \n",
       "156              7.5             196.0        1                 -1.0   \n",
       "182              7.0             222.0        1                 -3.0   \n",
       "232              7.0              75.0        1                 -5.0   \n",
       "262              7.0             105.0        1                 -7.0   \n",
       "290              6.4             133.0        0                 -9.0   \n",
       "319              6.2             162.0        1                -11.0   \n",
       "347              6.0             190.0        1                -13.0   \n",
       "376              6.8             219.0        1                -15.0   \n",
       "412              6.8              58.0        1                -17.0   \n",
       "449              6.8              95.0        1                -19.0   \n",
       "485              6.4             131.0        1                -21.0   \n",
       "522              6.4             168.0        1                -23.0   \n",
       "558              6.0             204.0        1                -25.0   \n",
       "595              6.0              44.0        0                -27.0   \n",
       "631              6.6              80.0        1                -29.0   \n",
       "668              6.6             117.0        0                -31.0   \n",
       "702              6.2             151.0        0                -33.0   \n",
       "736              6.2              47.0        0                -35.0   \n",
       "770              6.8              81.0        0                -37.0   \n",
       "816              5.8             127.0        0                -39.0   \n",
       "854              5.7             165.0        1                -41.0   \n",
       "892              5.6              65.0        0                -43.0   \n",
       "930              6.7             103.0        0                -45.0   \n",
       "968              6.5             141.0        0                -47.0   \n",
       "1017             5.3              52.0        0                -49.0   \n",
       "...              ...               ...      ...                  ...   \n",
       "288              7.3             131.0        1                 -7.6   \n",
       "289              7.0             132.0        1                 -8.4   \n",
       "316              7.0             159.0        1                 -9.2   \n",
       "317              7.0             160.0        1                -10.0   \n",
       "318              7.0             161.0        1                -10.8   \n",
       "345              7.0             188.0        0                -11.6   \n",
       "346              7.0             189.0        0                -12.4   \n",
       "373              7.0             216.0        1                -13.2   \n",
       "374              7.5             217.0        0                -14.0   \n",
       "375              7.4             218.0        0                -14.8   \n",
       "410              7.0              56.0        0                -15.6   \n",
       "411              7.0              57.0        0                -16.4   \n",
       "446              8.3              92.0        1                -17.2   \n",
       "447              8.3              93.0        1                -18.0   \n",
       "448              9.0              94.0        1                -18.8   \n",
       "483              8.3             129.0        0                -19.6   \n",
       "484              9.0             130.0        0                -20.4   \n",
       "519              8.3             165.0        0                -21.2   \n",
       "520              9.0             166.0        0                -22.0   \n",
       "521              9.0             167.0        0                -22.8   \n",
       "556              8.0             202.0        0                -23.6   \n",
       "557              8.0             203.0        0                -24.4   \n",
       "592              7.0              41.0        1                -25.2   \n",
       "593              7.0              42.0        1                -26.0   \n",
       "594              7.0              43.0        1                -26.8   \n",
       "629              7.0              78.0        1                -27.6   \n",
       "630              7.0              79.0        1                -28.4   \n",
       "665              7.0             114.0        0                -29.2   \n",
       "666              7.5             115.0        0                -30.0   \n",
       "667              7.4             116.0        0                -30.8   \n",
       "\n",
       "      ESPECIALIDAD  FUMADOR  VIVE_EN_PAREJA  TBURNOUT  HOSPITAL  Cluster  \n",
       "0                3      1.0                0      Yes         1        1  \n",
       "19              15      1.0                1       No         2        0  \n",
       "45              19      0.0                1       No         1        0  \n",
       "104             28      1.0                1      Yes         1        1  \n",
       "130             18      1.0                1      Yes         2        1  \n",
       "156             23      0.0                1       No         1        0  \n",
       "182             11      0.0                1      Yes         2        1  \n",
       "232             11      1.0                1      Yes         1        1  \n",
       "262              6      0.0                1      Yes         2        1  \n",
       "290              1      0.0                0      Yes         2        1  \n",
       "319             16      0.0                0      Yes         2        1  \n",
       "347             29      0.0                1      Yes         2        1  \n",
       "376             10      0.0                1       No         2        0  \n",
       "412             10      0.0                1       No         2        0  \n",
       "449              8      0.0                1       No         1        0  \n",
       "485              4      0.0                1      Yes         1        1  \n",
       "522             23      0.0                1      Yes         2        1  \n",
       "558             19      0.0                1       No         2        0  \n",
       "595             26      0.0                1      Yes         1        1  \n",
       "631             23      1.0                1       No         2        0  \n",
       "668             21      0.0                0      Yes         2        1  \n",
       "702             10      0.0                1       No         1        0  \n",
       "736              4      0.0                1      Yes         2        1  \n",
       "770             26      0.0                1      Yes         2        1  \n",
       "816             11      0.0                0      Yes         2        1  \n",
       "854              6      0.0                1      Yes         2        1  \n",
       "892             20      0.0                1      Yes         2        1  \n",
       "930              1      1.0                1      Yes         1        1  \n",
       "968             10      0.0                0      Yes         1        1  \n",
       "1017            17      0.0                1       No         2        0  \n",
       "...            ...      ...              ...      ...       ...      ...  \n",
       "288             14      0.0                1      Yes         2        1  \n",
       "289             15      0.0                1      Yes         2        1  \n",
       "316              4      0.0                1      Yes         2        1  \n",
       "317              7      0.0                1      Yes         2        1  \n",
       "318              6      0.0                1      Yes         1        1  \n",
       "345             18      1.0                1      Yes         2        1  \n",
       "346              1      1.0                1      Yes         2        1  \n",
       "373             19      0.0                1       No         1        0  \n",
       "374             20      0.0                1       No         1        0  \n",
       "375             21      0.0                1       No         1        0  \n",
       "410              6      0.0                1      Yes         2        1  \n",
       "411              8      0.0                1      Yes         2        1  \n",
       "446             26      0.0                1      Yes         2        1  \n",
       "447             36      0.0                1      Yes         2        1  \n",
       "448              1      0.0                1      Yes         2        1  \n",
       "483             19      1.0                1      Yes         2        1  \n",
       "484             20      1.0                1      Yes         2        1  \n",
       "519             39      0.0                1      Yes         2        1  \n",
       "520              4      0.0                1      Yes         2        1  \n",
       "521              7      0.0                1      Yes         2        1  \n",
       "556             17      0.0                1       No         2        0  \n",
       "557             18      0.0                1      Yes         2        1  \n",
       "592              4      0.0                1      Yes         2        1  \n",
       "593              7      0.0                1      Yes         2        1  \n",
       "594              6      0.0                1      Yes         2        1  \n",
       "629             18      1.0                1      Yes         1        1  \n",
       "630              1      1.0                1      Yes         1        1  \n",
       "665             10      0.0                1       No         2        0  \n",
       "666             19      0.0                1       No         2        0  \n",
       "667             20      0.0                1       No         2        0  \n",
       "\n",
       "[1349 rows x 17 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Evaluation\n",
    "#Create a new column for df called Cluster which value 1 for BURNOUT and 0 for not BURNOUT\n",
    "def converter(cluster):\n",
    "    if cluster=='Yes':\n",
    "        return 1\n",
    "    else:\n",
    "        return 0\n",
    "df['Cluster'] = df['TBURNOUT'].apply(converter)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[236 218]\n",
      " [439 456]]\n",
      "             precision    recall  f1-score   support\n",
      "\n",
      "          0       0.35      0.52      0.42       454\n",
      "          1       0.68      0.51      0.58       895\n",
      "\n",
      "avg / total       0.57      0.51      0.53      1349\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Create a confusion matrix and classification report\n",
    "from sklearn.metrics import confusion_matrix,classification_report\n",
    "print(confusion_matrix(df['Cluster'],kmeans.labels_))\n",
    "print(classification_report(df['Cluster'],kmeans.labels_))"
   ]
  }
 ],
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